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In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn incredibly complex data patterns. The deficiency of these methods, however, is their inability to explain the prediction process, making them untrustworthy and their use precarious in high-stakes situations. SHapley Additive exPlanations (SHAP) analysis is an explainable AI method growing in popularity for its ability to explain model predictions in terms of […]

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In 2025, the National Vulnerability Database published more than 48,000 new common vulnerabilities and exposures (CVEs), reflecting the impact of automated and AI-powered tools on vulnerability discovery. For security teams, however, knowing about new vulnerabilities isn’t enough; they must translate each disclosure into robust detection logic fast enough to protect large, complex systems. At AWS, we built RuleForge, an agentic-AI system that generates detection rules directly from examples of vulnerability-exploiting code, achieving a 336% productivity advantage over manual […]

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Despite deep learning models running well-defined mathematical functions, we lack a formal mathematical framework for describing model architectures. Ad-hoc notation, diagrams, and pseudocode poorly handle nonlinear broadcasting and the relationship between individual components and composed models. This paper introduces a categorical framework for deep learning models that formalizes broadcasting through the novel axis-stride and array-broadcasted categories. This allows the mathematical function underlying architectures to be precisely expressed and manipulated in a compositional manner. These mathematical definitions are translated […]

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While Learned Data Compression (LDC) has achieved superior compression ratios, balancing precise probability modeling with system efficiency remains challenging. Crucially, uniform single-stream architectures struggle to simultaneously capture micro-syntactic and macro-semantic features, necessitating deep serial stacking that exacerbates latency. Compounding this, heterogeneous systems are constrained by device speed mismatches, where throughput is capped by Amdahl’s Law due to serial processing. To this end, we propose a Dual-Stream Multi-Scale Decoupler that disentangles local and global contexts to replace deep serial […]

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We provide a computational complexity lens to understand the power of machine learning models, particularly their ability to model complex systems. Machine learning models are often trained on data drawn from sampleable or more complex distributions, a far wider range of distributions than just computable ones. By focusing on computable distributions, machine learning models can better manage complexity via probability. We abstract away from specific learning mechanisms, modeling machine learning as producing P/poly-computable distributions with polynomially-bounded max-entropy. We […]

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Assignement2: Deep Learning-Based Quiz (Visual MCQ Solver) You will be given PNG images containing questions from deep learning Your tasks: Process and understand questions from images Build a model to answer MCQs Each question will have 4 options with only 1 correct answer can someone tell me how i can solve this task i mean i have image which contain textual question can include equation also i dont know what is best way to solve this task if […]

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